Automated Pressure Transient Analysis: A Cloud-Based Approach

Abou-Sayed,A., Abou-Sayed,O., Guo,Y., Panchal,Y., Zidane,A.
Presented at: International Petroleum Technology Conference, Beijing, China, March 2019.


Pressure transient analysis provides useful information to evaluate injection induced fracture geometry, permeability damage near wellbore and pressure elevation in injection zone. Manual analysis of pressure data after each injection cycle could be subjective and time-consuming. In this study a cloud-based approach to automatically analyze pressure data will be presented, which is aimed to improve the reliability and efficiency of pressure transient analysis. There are two fundamental requirements for the automated pressure transient analysis: 1) Pressure data needs to be automatically retrieved from field sites and fed to analyzer; 2) Analyzer can automatically select instantaneous shut-in pressure (ISIP), identify flow regimes, and determine fracture closure point. To meet these requirements and also take the advantages of cloud storage and computing technologies, a web based application has been developed to pull real time injection data from any field sites and push it to a cloud database. Besides analyzing any existing pressure data in the cloud database, a built-in pressure transient analyzer can also detect any real-time pressure data and perform pressure analysis automatically when required data is available. The automated, cloud-based pressure transient analysis has been applied to multiple injection projects. In general, the analysis results including permeability, fracture half length, skin factor, and fracture closure pressure are comparable to these yielded from manual analysis. The discrepancy is mainly caused by poor data quality. The inconsistent selections of ISIP and different slopes defined for G-function and flow regime analyses also contribute to the divergence. Overall, the automated pressure transient analysis provides consistent results as the exact same criteria are applied to the pressure data, and analysis results are independent on analyzer’s experience and knowledge. In addition, machine learning algorithms are applied to continuously refine the criteria and improve the quality of analysis results. As data from oil/gas industry increases exponentially over time, automated data transmission, storage, analysis and access are essential to maximize the value of the data and reduce operation cost. The automated pressure transient analysis presented here demonstrates that cloud storage and computing combined with automated analysis tools is an optimal way to overcome big data challenges facing by oil/gas industry.